67 research outputs found
Exploiting informative priors for Bayesian classification and regression trees
A general method for defining informative priors
on statistical models is presented and applied
specifically to the space of classification and regression
trees. A Bayesian approach to learning such
models from data is taken, with the Metropolis-
Hastings algorithm being used to approximately
sample from the posterior. By only using proposal
distributions closely tied to the prior, acceptance
probabilities are easily computable via marginal
likelihood ratios, whatever the prior used. Our approach
is empirically tested by varying (i) the data,
(ii) the prior and (iii) the proposal distribution. A
comparison with related work is given
Improving numerical reasoning capabilities of inductive logic programming systems
Inductive Logic Programming (ILP) systems have been largely applied to classification problems with a considerable success. The use of ILP systems in problems requiring numerical reasoning capabilities has been far less successful. Current systems have very limited numerical reasoning capabilities, which limits the range of domains where the ILP paradigm may be applied. This paper proposes improvements in numerical reasoning capabilities of ILP systems. It proposes the use of statistical-based techniques like Model Validation and Model Selection to improve noise handling and it introduces a new search stopping criterium based on the PAG method to evaluate learning performance. We have found these extensions essential to improve on results mer statistical-based algorithms for time series forecasting used in the empirical evaluation study
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